Systems employed in generating guides, or information regarding available options in connection with a particular activity, may produce suggestions or recommendations for the user. Examples of such systems include on-line shopping or information retrieval systems and systems for delivery of content, particularly entertainment content such as audio or video programs, games and the like. In the case of systems delivering entertainment content, automatic action may be triggered by the generation of a suggestion or recommendation, such as caching, during a period when the entertainment content is not being utilized by the user, at least a portion of available entertainment content for later presentation to the user.
In generating suggestions or recommendations, suitable results are most often obtained by employing, at least in part, an explicit user profile of likes and dislikes. In general, such explicit user profiles are generated by user access and completion of a profiling questionnaire, within which the user rates various meta-data descriptors such as (for video content) genre, actor(s), director, title, etc.
Populating or developing an explicit user profile typically must be initiated by the user, and often requires (or allows) users to independently enter values for meta-data descriptors, such as an actor's name or the title of video content. This forces the user to attempt to remember, at the time of profile creation, all relevant values for meta-data descriptors on which actions employing the profile should be based, which is difficult if not impossible.
On the other hand, displaying a list of all possible meta-data descriptor values to the user, from which selections may be made to populate the user's profile, will generally result in the user having to review a list of unwieldy size, or risk missing suitable descriptors. Particularly for cross-media systems (i.e., video, audio and/or other content), the user might be required to select and/or rate items from a list containing tens of thousands of entries. Either alternative (requiring the user to recall relevant items or presenting the user with a comprehensive list), or even a combination of the two approaches, is unduly demanding on the user and requires more time than a user is likely to be willing to spend on the task, and is therefore unsatisfactory.
A quick and effective technique for initializing a user profile involves stereotypes derived from analysis of the viewing patterns of a multitude of users. The user selects a stereotype or set of stereotypes to initialize the profile, and thereafter provides feedback to the system in order to guide the system on how to lower the “error” rate and make better suggestions. For video program recommendation systems, for example, the feedback is often in the form of a “Yes, I liked the show” or “No, I didn't like the show,” perhaps with varying degrees of intensity.
Not all feedback from the user, however, will improve the system's error rate. A particular piece of feedback might improve the user understanding in one area while making it worse in one or more other area(s). The Combined effect, measured in terms of error rate, could be worse, overall.
There is, therefore, a need in the art for improving use of feedback to adapt a stereotype to user preferences in a recommendation system.